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It is often only after complex investments have been made that it is realized that minor or major insufficiencies of the installed
equipment cost mony every day. This can be avoided by simulation-supported detailed planning of the future plant configuration.
Potential uncertainties in connection with investments in plant expansions can be drastically reduced. Once the right plant
constellation has been found and realized, it is even possible to install a diagnostic function based on physical data.

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The purpose of the simulation model was to test different modernization variants. However, before that the quality of the model had to be proved.
This was done by a retrospective simulation of production in a past quarter. The input data for the simulation system - which were attached exact
time stamps - were generated from data of the material tracking system.
The results were highly convincing. The simulator had modelled plant performace so precisely that after a simulated production period of aber 2.5
months the calculated production deviated from reality by only two minutes.
Then the modernization propolals were simulated with the new plant components installed at different positions and with different buffer zones.
The variant which perfomed best - also in consideration of shift manning and crane capacities - was selected.

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Capacity expansion of the Alouette aluminium smelter

The capacity of the Alouette aluminium smelter in Canada, which belongs to the Hydro Group, was to be more than doubled by a major investment
programme. It is shown in figure 2
before changes are undertaken. The logistical and technical feasibility of the investment was underpinned
by a simulation.

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Poses++ simulated the consequences of a doubling of the plant capacity in a scenario in which almost 600 smelting pots arranged in four potlines
are services by about 20 crances and about the same number of vehicles. Among others, due to the fact that is was possible to calculate the
performance of the plant several month in advance, the plant extension could be brought on stream three months earlier than scheduled. The
ramp-up curve took a virtually optimal course. In the event of failures or trouble, consequences and alternatives could be calculated in no time.

Why is Poses++ so efficient?

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Poses++ is based on a graph-theoretic approach. A self-learning algorithm computes only those areas of the models where changes are possible. Based
on this the computation times grow linearly with the model size, not exponentially as with conventional software tools.
In the steel and non-ferrous industries, Saarstahl, Dillinger Hütte, Buderuns, Degussa, Hydro Aluminium Deutschland (formerly VAW) have already
successfully employed the Poses++ software. In metallurgical and rolling mill engineering, SMS Demag uses Poses++ in automation technology to enable
plug&work acceptance tests.